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DNA Microarrays

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Related Experiment Video

Updated: May 2, 2026

Lung microRNA Profiling Across the Estrous Cycle in Ozone-exposed Mice

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Published on: January 7, 2019

Missing value imputation for microarray data: a comprehensive comparison study and a web tool.

Chia-Chun Chiu, Shih-Yao Chan, Chung-Ching Wang

BMC Systems Biology

|February 26, 2014

Summary

This summary is machine-generated.

Accurate estimation of missing values in microarray data is crucial for downstream analyses. Local-least-squares-based methods are recommended for most microarray datasets, improving data analysis performance.

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Last Updated: May 2, 2026

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Published on: June 24, 2021

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Microarray data frequently contain missing values, hindering downstream analyses.
Existing missing value imputation algorithms lack comprehensive performance comparisons.
Incomplete evaluations limit the selection of optimal imputation methods.

Purpose of the Study:

To conduct a thorough comparison of microarray missing value imputation algorithms.
To evaluate algorithm performance across diverse datasets and species.
To identify the most effective imputation strategies for microarray data.

Main Methods:

Compared nine imputation algorithms using thirteen benchmark microarray datasets.
Conducted 110 independent simulation runs for robust evaluation.
Assessed performance using statistical and biologically meaningful measures.

Main Results:

Imputation algorithm performance is primarily dependent on dataset type, not species origin.
Local-least-squares-based methods demonstrated strong performance across most datasets.
Biologically relevant measures are crucial for evaluating imputation impact.

Conclusions:

This study provides a comprehensive comparison to guide optimal algorithm selection.
A standardized comparison strategy is proposed for future algorithm development.
The MissVIA web tool (http://cosbi.ee.ncku.edu.tw/MissVIA) offers easy-to-use imputation services.